3 citations · 11 across the 9 of their papers we have counts for
3 papers · 1 filter
Learning Weakly Convex Sets in Metric Spaces
Eike Stadtländer, Tamás Horváth, Stefan Wrobel
One of the central problems studied in the theory of machine learning is the question of whether, for a given class of hypotheses, it is possible to efficiently find a {consistent}…
A Generalized Weisfeiler-Lehman Graph Kernel
Till Hendrik Schulz, Tamás Horváth, Pascal Welke +1
The Weisfeiler-Lehman graph kernels are among the most prevalent graph kernels due to their remarkable time complexity and predictive performance. Their key concept is based on an…
A Novel Regression Loss for Non-Parametric Uncertainty Optimization
Joachim Sicking, Maram Akila, Maximilian Pintz +3
Quantification of uncertainty is one of the most promising approaches to establish safe machine learning. Despite its importance, it is far from being generally solved, especially…